Archive for January, 2010

Ontology in computer science is a formal representation of concepts within a domain. With the emergence of the semantic web, ontologies will take the role of the anchor to which all content can and should relate.

Ownership

If their purpose is indeed to serve as the lighthouse on the sea of information, ontologies must be unambiguous, and therefore be defined and maintained by a single entity. Will this single entity be a company, a consortium, a committee, an organization or perhaps a government agency? What guarantees that formal ontologies will follow the changes that may occur in the instance domain?

Collective definition

There is one guarantee: collective ontology management. And I’m not thinking of Wikipedia-style collaboration, but real collective effort where everyone throws in his/her two cents.

Take a look at this very simple comparison between equivalent fractions of a content map and an ontology. (A content map is based on collective definition of probabilistic relations between content elements.)

The resemblance is hard to miss. It’s no surprise, both deal with concepts and instances bound into a network through different sorts of relations. But as we take a closer look, it becomes obvious that content mapping is fundamentally different.

There’s no distinction between elements such as classes, instances, attributes, et cetera. They’re all content. What constitutes a class from an ontology point of view depends solely on the relation. One element may be instance and class simultaneously.

There are fewer, more general types of connections. You can extend an ontology with new relations that specify the way certain elements are connected to each other. Content mapping defines only a few, from which new, implicit ones can be derived via machine learning.

Domains don’t have definite borders. It is very likely that elements have connections leading out of a domain, superseding what we call ontology alignment. As an element may be instance and class at the same time, it can also belong to more than one domain. In fact, these are the connections through which cross-ontology relationships emerge.

Dynamics is inherently embedded into the system. As content changes, connections follow. Classes are constantly created, updated or deleted by changing generalization connections.

Content mapping creates an organic system where ontologies float on the surface.

Defining ontologies in this environment is no longer necessary, they crystallize with the natural progress. We only have to harvest the upper generalization layers to get an understanding of conceptual connections in any data set. Domains needn’t be defined beforehand either. Instead, we draw their outlines where we deem them fitting.

Clues to rely on

However flexible content mapping technology may seem in defining and following ontologies, its purpose is to connect previously unconnected content, and therefore it needs clues to follow up. Prior user input, search indexes, or existing ontologies may provide these clues. Once those clues are there, content mapping simplifies ontology management in several aspects.

Fewer relations: Only a handful of general relations are explicit, domain specific relations are all derived from those.

No need for focused attention: Ontology management requires no supervision as implicit connections change with content.

No knowledge of semantics: Connections (both explicit and implicit) can be set or changed without any knowledge on the subject of semantics or ontologies.

Stochastic linguistics deals with the probabilities of certain patterns occurring in natural language and is therefore very likely to play an important role in future natural language processing (NLP) applications, including the semantic web.

… the leading edge of web technology developing towards globalized information interflow is almost exclusively based on stochastic technologies.

Stochastic techniques

Stochastic techniques, such as n-gram and latent semantic analysis (LSA) help us identify and classify patterns in natural language, through which we are able to compare, search and analyze documents in a language independent manner.

However, they fail to further machine understanding beyond a broad structural analysis. These techniques recognize a very limited set of relationships between terms, that usually narrow down to: “identical” or “interchangeable”. These relations say nothing about the quality of the connection, i.e. whether two similar terms are similar in structure or meaning.

This is where a catch 22 begins to unfold. In order to provide relations reflecting on the meaning of terms by which they can be understood, machines running stochastic analyses would have to understand those terms first. The only way of resolving this “catch” leads through adding human intelligence to the mix, extending the network of terms with the missing semantic links, which is how we arrive at content mapping.

Mapping language

Content mapping is a system that does exactly the above by collectively defining and maintaining a rich set of relations between bits of content, including natural language patterns. Relations create equivalence classes for content elements where one term may belong to several classes based on its meaning, structure and function. Synonymy and polysemy that are hinted by LSA for instance, are not only explicitly defined in content mapping, but extended by relations vital to machine understanding, like generalization and identical meaning.

Let’s see an example. The figure below places the term “it’s 5 o’clock” in a content map. Colored connections are based on the votes of people participating in the mapping process. Outlined white arrows represent generated connections. Similarly, bubbles with solid outlines are actual pieces of content (terms), ones with dotted outlines are generated.

In the map, different relations contribute to language understanding in different ways.

Generalizations help conceptualizing terms.

Responses indicate contextual relationships between terms by connecting effects to their causes or answers to questions.

Identical meaning creates pathways for terms with low number of connections to other relations such as generalizations or responses.

Abstractions extract structural similarity from terms to be used later by pattern recognition.

It takes two

Even though we obtain richer and more reliable information about term relationships through content mapping, it would take a lot of guesswork before actually related terms would get connected. To reduce the amount of unnecessary passes, LSA could provide higher-than-normal error rate connections as clues for the content mapping process to follow up.

A composite solution that unites the two, could point to a direction where a language independent structural and semantic understanding of text finally comes within our reach.

Inspired by this post of Chris Dixon, I summarized my thoughts on the future of the web in a single tweet like this:

The fundamental question that will shape the future of the web is how we deal with entropy.

Options

The disorder of the web thrives on both content and their connections. Today’s approach to the web of tomorrow depend on how we address this issue. The figure below shows what we can expect as a result of different combinations of low and high entropy in the two layers.

Entropy in the content layer reflects the degree of internal disorder. If we choose to lower content entropy through the addition of relevant metadata or structure we’ll realize the semantic web. If we don’t then content will remain unorganized and we’ll end up in the noisy web.

Entropy in the connection layer expresses disorder in the network of content. By defining meaningful relations between content elements connection entropy will decrease leading to the synaptic web. Should we leave connections in their ad-hoc state, we’ll arrive in the unorganized web.

The study of web entropy becomes interesting when we take a look at the intersections of these domains.

Semantic – synaptic: The most organized, ideal form of the web. Content and connections are thoroughly described, transparent and machine readable. Example: linked data.

Semantic – unorganized: Semantic content loosely connected throughout the web. Most blog posts have the valid semantic structure of documents, however, they’re connected by hyperlinks that say nothing about their relation. (Say, whether a blog entry extends, reflects on or debates the linked one.)

Noisy – unorganized: Sparse network of unstructured content. This is the domain we’ve known for one and a half decades where keyword based indexing and search still dominates the web. If it continues to develop in this direction then technologies such as linguistic parsing and topic identification will definitely come into play in the future.

Which one?

The question is obvious: which domain represents the optimal course to take? Based on the domains’ description semantic – synaptic seems to be the clear choice. But we’re discussing entropy here and from thermodynamics we know that entropy grows in systems that are prone to spontaneous change and order is restored only at the cost of energy and effort.

Ultimately, the question comes down to this: are we going to fight entropy or not?

Bringing the semantic web into existence is an enormous task. To me, fighting the reluctance of people to adopt the use of metadata and semantic formats is unimaginable. The synaptic web seems more feasible as the spreading of social media already indicates. But in the end what matters is which domain or combination of domains will be popular among early adopters. The rest will follow.

Advertising is the number one option to monetize content on the web. Even with the advent of the real-time web, until a viable model of in-stream advertising is conceived, search engines and their means of online marketing, such as SEO, AdWords and AdSense remain dominant. As in-stream and other real-time web marketing models mature, become relevant and non-intrusive, search engines will have to undergo fundamental change to keep a significant segment of the market.

Keywords are bad

In order to induce fundamental change first we have to identify the fundamental flaw. At the dawn of the World Wide Web the paradigm of search was borrowed from text documents where a certain paragraph is easily spotted by looking up a few words we presume to be in it.

With more extensive content and less prior knowledge about it this paradigm became harder and harder to apply. However, in our efforts to rank content by its structure, context and user preferences we kept keywords all along. Moreover, keywords today fuel an entire industry of online advertising, recklessly overlooking the distortion they add between content and the user’s specific preferences.

To make search and search-based ads as relevant and as non-intrusive as the real-time web has to offer, keywords must be forgotten once and for all.

Content mapping

Content mapping connects units of content directly by user interaction via a rich, irreducible set of relations. Between your content and others there may be similarities, equivalence, references and other sorts of relations of various significance and strength (relevance). Content that is relevant to yours make up its ideal context. The first n results a search engine returns is on the other hand the actual context.

The distance between the ideal and actual context marks the accuracy of a search engine.

Now, when you’re searching with Google, you’re basically trying to define the ideal context for the content you need. Imagine just how clumsy and inefficient it is to do through a couple of keywords.

Using a content mapping engine you type in a piece of content, not context. That content (or one that’s semantically identical) is probably already placed and centered in its ideal context. You’ll receive the elements inside as results in decreasing order of relevance.

SEO

Search engine optimization is an attempt to match the actual context to the ideal. Inevitably, when you tune your webpage for certain keywords you’re guaranteed to bolt it into the wrong context.

With content mapping, there’s no need for SEO. Not in a fair use scenario anyway, but on the other hand, the well-known SEO exploits (black hat, article spinning, keyword stuffing) obviously won’t work either. If the actual context of your content changes, it will re-position itself automatically to a new context that approximates the ideal as close as possible.

Shooting in the dark

When it comes to online marketing, SEO is just one of your options. Ranking algorithms may change and your content gets easily ripped out of the context you worked on so hard to match. So, you turn to a different, somewhat more reliable marketing tool, AdWords for example.

What happens from then on is again viewed through the smudgy glass of keywords. First, you take a wild guess at what keywords will best match your ideal context, bid for them and see what happens. If the conversion rates are not satisfactory, repeat the process until you get the best achievable results.

Assuming your campaign was successful, along the way you’ve probably

lost a lot of time tweaking

lost potential customers / deals

paid for the wrong keywords

took an exam or hired a consultant

and ended up in a wrong context anyway

In a content mapping environment however, you land at the center of your ideal context. With no tweaking, no time nor money lost.

What’s the catch?

I’ve hinted in the definition that content mapping relies on user input. In fact it relies on almost nothing but that. I admit that building and maintaining the connection index takes huge collective efforts, but I’m convinced about its feasibility.

We only have to make sure it

Provides frictionless tools for contribution: When the entire index has to be collected from the network it’s vital for the process not to demand more time and attention from contributors than what’s necessary.

Treats harmful activity as noise: Random noise is natural in content mapping. Useful information within the system – however small percentage – is expected to be coherent and thus extractable. In order to suppress useful information a successful attack would have to insert harmful information of at least equal coherence. Input gathering tools within the system must be designed with that in mind.

Regardless of how cleverly we gather information from the network, latency remains an integral property of content mapping. Changing actual context needs time to catch up to the ideal depending on the size of the network. The bigger it is, the faster the response. At the start of a campaign one must be clear with the delay by which the content gets centered in its context.

Unfortunately neither of the concerns above are comparable to building the network in terms of size and difficulty. However, the steps through which this can be achieved are yet to be defined.

Updates

Click-through rates: It’s sort of self explaining, but it may be necessary to emphasize the following. When a piece of content is centered in its ideal context it will yield the highest click-through rates when placed on a blog or website in an AdSense fashion.

Similar solutions: MyLikes has implemented a system in which advertisers may reach a higher click-through rate by placing their ads next to (or embedded into) relevant content produced by trusted “influencers”.

In-stream solutions: Take a look at this list of Twitter-based marketing tools on oneforty. They may not be all in-stream, but they can give you a general idea of advertising in the real-time web.

Originally this post started out as an analysis of collective intelligence in Asimov‘s Foundation universe just to see if my model is applicable to fictional entities as well. By the time I finished the first draft I realized it all boiled down to a new collective consciousness property: robustness.

Following my original plan, I’m going to start by examining the two kinds of collective consciousness that form the parallel and competing alternatives for the development of the Galaxy.

Gaia

Asimov’s Gaia is a planet-size autonomous collective entity formed by every living and inanimate objects connected to it. Gaia’s consciousness is so strong that for anything to become a part of it has to be ingested first through an energy-consuming process. Once an object or being becomes a part of Gaia, its own consciousness is added to the whole, and from then on contributes to the collective wisdom, memory and actions of Gaia. The ingestion process is reversible: one may leave the collective of Gaia by severing the connection either by distance or becoming a part of a non-Gaia system. Death doesn’t lead out of Gaia, only decreases its overall consciousness, which is kept in balance by the birth of new highly conscious components.

To see the Asimovian Gaia in context, I’m going to use the Lovelockian Gaia as reference in its analysis.

Quantitative analysis

Asimov’s Gaia and the Lovelockian Gaia share most of their properties in the Collective Entity Space (CES). The only obvious difference is in their level of awareness. In the Asimovian Gaia one is not only aware of the collective consciousness and his/her/its connection to it, but also actively accessing and using it for individual and global purposes.

Gaia

Components

Engagement

Awareness

Nature

Technology

Lovelockian

Entire planet

Passive

Unaware

Mixed

None

Asimovian

Entire planet

Passive

Aware

Mixed

None

Qualitative analysis

Through Asimov’s story we get to know a lot more about Gaia than we know about the collective consciousness of the Gaia Theory. The only Consciousness Classification Criteria (CCC) property that we don’t know is control, probably because of its irrelevance to the deeply integrated Gaians.

Gaia consciously maintains ecological balance that best suits the needs of its habitants.

The mental well-being and happiness of Gaians is credited to collective consciousness.

Gaia

Communication

Problem solving

Maturity

Usefulness

Control

Lovelockian

?

?

Infant / mature

Life

?

Asimovian

Telepathy

Multiple

Mature

Multiple

?

In a way, the Asimovian Gaia is an evolved Lovelockian Gaia.

Foundation

Although it’s not explicitly referred to as a collective consciousness in the novels, the First Foundation unquestionably shows the expected characteristics. The existence of the Second Foundation is the proof: it communicates with it, controls it and uses it for a purpose (the hastened establishment of a stable second Galactic Empire).

Quantitative analysis

The First Foundation just as any collective entity, can be placed in the CES. If you’ve read the series you know about the importance of unawareness. The science of psychohistory and the control exerted by the Second Foundation don’t work if the First Foundation becomes aware that they are observed or manipulated in any way. This translates to the CES as a small nudge on the awareness axis.

Components

Engagement

Awareness

Nature

Technology

The galaxy’s human population

Passive

Unaware

Psychical / mixed

None

Qualitative analysis

The First Foundation, as a collective entity (galactic consciousness) is communicated with by the Second Foundation through their mental powers and the mathematics of psychohistory. It needs to be under constant control in order to keep it on track and have its purpose fulfilled, hence it’s reasonable to assume that it’s still in a state of infancy.

Communication

Problem solving

Maturity

Usefulness

Control

Psychohistory

?

Infant

Social development

Second Foundation

The Mule

An analysis of the Foundation can’t be thorough without a glimpse on the Mule and his actions. The Mule is a mutant individual with unusual mental powers which he uses to manipulate the ruling class of the First Foundation and take over the Galaxy.

In terms of the CES he changes the behavioral structure of the entity’s components by altering their engagement and introducing a technology (governing by his mental influence) into the entity.

The Second Foundation

The Mule’s search for the Second Foundation during his struggle to rule the Galaxy draws the First Foundation’s attention to it, who, feeling threatened by its mere existence, wipes out the group that is believed to be its members. Only by the purposeful and delicate planning of the Second Foundation is its total destruction avoided, unawareness restored and the galactic consciousness returned to its previous state.

Robustness

If we compare Asimov’s two collective consciousnesses we’ll see that there’s a fundamental difference in how they react to perturbation in the CES. While Gaia is either indifferent to it or responds by returning to (near) its original position, the Foundation could be ruined by a small shift on the awareness axis, and returned only at extreme difficulties. Gaia therefore is a robust collective entity, while the First Foundation is a fragile one.

The robustness property of a collective consciousness reflects the probability of returning to its original state in the Collective Entity Space in response to perturbation.

Let’s take a look at the examples I used in the previous posts. The following table shows the expected reaction and robustness of a collective entity to perturbation on the given axis.

Entity

Engagement

Nature

Technology

Robustness

Weak Gaia

–

?

–

robust

GCP

–

collapses

communication collapses

moderately fragile

Twitter

collapses

collapses

collapses

fragile

Note that components and awareness are not included in the table as all three entities are indifferent to small changes along those axes. Adding or removing a few components, as well as awareness, even when spread among components don’t have an impact on any of them.

Through robustness we are able to measure the sustainability of a collective entity. The examples from Asimov’s Foundation indirectly indicate its importance by the choice made in the story between the Foundation and a galactic Gaia, “Galaxia” in favor of the latter. Golan Trevize, the protagonist of Foundation’s Edge, unconsciously chooses the robust collective entity that ensures the long-term safety of humankind that is scattered throughout the galaxy.